Overview

Dataset statistics

Number of variables12
Number of observations2778
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory282.1 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with total_orders and 3 other fieldsHigh correlation
total_orders is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
total_products_ordered is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
total_quantity is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size_quantity is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_basket_size_productsHigh correlation
avg_basket_size_products is highly overall correlated with total_products_ordered and 1 other fieldsHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly skewed (γ1 = 42.54355149)Skewed
avg_ticket is highly skewed (γ1 = 27.7027396)Skewed
qty_returns is highly skewed (γ1 = 21.64504304)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.2%) zerosZeros
qty_returns has 1481 (53.3%) zerosZeros

Reproduction

Analysis started2023-08-11 00:15:37.316985
Analysis finished2023-08-11 00:15:46.746406
Duration9.43 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2778
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15284.845
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:46.782735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12625.85
Q113814.25
median15242.5
Q316779.75
95-th percentile17950.15
Maximum18287
Range5940
Interquartile range (IQR)2965.5

Descriptive statistics

Standard deviation1715.6364
Coefficient of variation (CV)0.11224428
Kurtosis-1.206922
Mean15284.845
Median Absolute Deviation (MAD)1484
Skewness0.016456349
Sum42461299
Variance2943408.3
MonotonicityNot monotonic
2023-08-10T21:15:46.849687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17930 1
 
< 0.1%
15813 1
 
< 0.1%
17058 1
 
< 0.1%
17704 1
 
< 0.1%
16933 1
 
< 0.1%
13772 1
 
< 0.1%
16249 1
 
< 0.1%
14198 1
 
< 0.1%
13989 1
 
< 0.1%
Other values (2768) 2768
99.6%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18265 1
< 0.1%
18263 1
< 0.1%
18261 1
< 0.1%
18260 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2763
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2840.6605
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:46.916143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile262.567
Q1626.585
median1165.93
Q32419.6075
95-th percentile7461.5245
Maximum279138.02
Range279101.46
Interquartile range (IQR)1793.0225

Descriptive statistics

Standard deviation10457.915
Coefficient of variation (CV)3.6815081
Kurtosis373.4173
Mean2840.6605
Median Absolute Deviation (MAD)687.275
Skewness17.111453
Sum7891354.8
Variance1.0936798 × 108
MonotonicityNot monotonic
2023-08-10T21:15:46.977405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.44 2
 
0.1%
734.94 2
 
0.1%
598.2 2
 
0.1%
1078.96 2
 
0.1%
379.65 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
2053.02 2
 
0.1%
1314.45 2
 
0.1%
331 2
 
0.1%
Other values (2753) 2758
99.3%
ValueCountFrequency (%)
36.56 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
77.4 1
< 0.1%
84.65 1
< 0.1%
90.3 1
< 0.1%
93.35 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

recency_days
Real number (ℝ)

ZEROS 

Distinct253
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.924406
Minimum0
Maximum373
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.044230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum373
Range373
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.851053
Coefficient of variation (CV)1.2095173
Kurtosis3.4605459
Mean56.924406
Median Absolute Deviation (MAD)24
Skewness1.9029993
Sum158136
Variance4740.4675
MonotonicityNot monotonic
2023-08-10T21:15:47.110497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.6%
4 87
 
3.1%
2 85
 
3.1%
3 85
 
3.1%
8 76
 
2.7%
10 67
 
2.4%
9 66
 
2.4%
7 65
 
2.3%
17 62
 
2.2%
22 55
 
2.0%
Other values (243) 2031
73.1%
ValueCountFrequency (%)
0 33
 
1.2%
1 99
3.6%
2 85
3.1%
3 85
3.1%
4 87
3.1%
5 43
1.5%
7 65
2.3%
8 76
2.7%
9 66
2.4%
10 67
2.4%
ValueCountFrequency (%)
373 1
 
< 0.1%
372 1
 
< 0.1%
366 1
 
< 0.1%
360 1
 
< 0.1%
358 3
0.1%
354 1
 
< 0.1%
337 1
 
< 0.1%
336 2
0.1%
334 1
 
< 0.1%
333 2
0.1%

total_orders
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0475162
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.181365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0662295
Coefficient of variation (CV)1.4991658
Kurtosis184.14404
Mean6.0475162
Median Absolute Deviation (MAD)2
Skewness10.629927
Sum16800
Variance82.196517
MonotonicityNot monotonic
2023-08-10T21:15:47.248757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
28.2%
3 499
18.0%
4 393
14.1%
5 237
 
8.5%
6 173
 
6.2%
7 138
 
5.0%
8 98
 
3.5%
9 69
 
2.5%
10 55
 
2.0%
11 54
 
1.9%
Other values (45) 278
 
10.0%
ValueCountFrequency (%)
2 784
28.2%
3 499
18.0%
4 393
14.1%
5 237
 
8.5%
6 173
 
6.2%
7 138
 
5.0%
8 98
 
3.5%
9 69
 
2.5%
10 55
 
2.0%
11 54
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

total_products_ordered
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.59071
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.319879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile399.45
Maximum7838
Range7836
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.61455
Coefficient of variation (CV)2.1422411
Kurtosis337.20251
Mean129.59071
Median Absolute Deviation (MAD)45
Skewness15.356421
Sum360003
Variance77069.838
MonotonicityNot monotonic
2023-08-10T21:15:47.386292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 38
 
1.4%
35 35
 
1.3%
27 30
 
1.1%
29 30
 
1.1%
26 30
 
1.1%
25 28
 
1.0%
31 27
 
1.0%
19 27
 
1.0%
15 27
 
1.0%
33 26
 
0.9%
Other values (457) 2480
89.3%
ValueCountFrequency (%)
2 11
0.4%
3 12
0.4%
4 16
0.6%
5 16
0.6%
6 25
0.9%
7 14
0.5%
8 14
0.5%
9 19
0.7%
10 19
0.7%
11 23
0.8%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

total_quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct1638
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1669.085
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.456940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119
Q1330
median700
Q31478
95-th percentile4605.25
Maximum196844
Range196842
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation5885.7377
Coefficient of variation (CV)3.526326
Kurtosis486.26094
Mean1669.085
Median Absolute Deviation (MAD)451
Skewness18.191493
Sum4636718
Variance34641909
MonotonicityNot monotonic
2023-08-10T21:15:47.524632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
246 8
 
0.3%
150 8
 
0.3%
493 7
 
0.3%
219 7
 
0.3%
1200 7
 
0.3%
200 7
 
0.3%
516 7
 
0.3%
272 7
 
0.3%
260 7
 
0.3%
Other values (1628) 2702
97.3%
ValueCountFrequency (%)
2 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
27 2
0.1%
30 1
< 0.1%
32 1
< 0.1%
33 2
0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053223357
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.595468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0087463557
Q10.015810277
median0.024390244
Q30.041882808
95-th percentile0.11764706
Maximum17
Range16.99455
Interquartile range (IQR)0.026072532

Descriptive statistics

Standard deviation0.34733528
Coefficient of variation (CV)6.5259935
Kurtosis2046.3274
Mean0.053223357
Median Absolute Deviation (MAD)0.010697475
Skewness42.543551
Sum147.85449
Variance0.1206418
MonotonicityNot monotonic
2023-08-10T21:15:47.660405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.6%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.02127659574 13
 
0.5%
0.07692307692 13
 
0.5%
0.02564102564 13
 
0.5%
Other values (1215) 2630
94.7%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
0.2%
1.142857143 1
 
< 0.1%
1 8
0.3%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

avg_basket_size_quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct1937
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.18451
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.730690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.2625
median171.92857
Q3278.07143
95-th percentile584.95
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)174.80893

Descriptive statistics

Standard deviation261.58041
Coefficient of variation (CV)1.131479
Kurtosis115.58811
Mean231.18451
Median Absolute Deviation (MAD)81.166667
Skewness7.7180437
Sum642230.57
Variance68424.312
MonotonicityNot monotonic
2023-08-10T21:15:47.794301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
86 9
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
208 7
 
0.3%
197 7
 
0.3%
73 7
 
0.3%
82 7
 
0.3%
136 7
 
0.3%
105 7
 
0.3%
Other values (1927) 2700
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
11.875 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%
2000 1
< 0.1%
1903.5 1
< 0.1%
1866.933333 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2776
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.086257
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.862245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.8536955
Q112.44788
median17.953447
Q325.019036
95-th percentile87.543921
Maximum4453.43
Range4451.2794
Interquartile range (IQR)12.571157

Descriptive statistics

Standard deviation107.53578
Coefficient of variation (CV)3.3514591
Kurtosis1056.5006
Mean32.086257
Median Absolute Deviation (MAD)6.3234675
Skewness27.70274
Sum89135.622
Variance11563.944
MonotonicityNot monotonic
2023-08-10T21:15:47.926422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
25.6761194 1
 
< 0.1%
44.95564103 1
 
< 0.1%
32.59775 1
 
< 0.1%
19.03048387 1
 
< 0.1%
28.55451613 1
 
< 0.1%
12.80068182 1
 
< 0.1%
6.396214689 1
 
< 0.1%
Other values (2766) 2766
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%
602.4531323 1
< 0.1%

avg_basket_size_products
Real number (ℝ)

HIGH CORRELATION 

Distinct897
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.12752
Minimum0.2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:47.991377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.5113636
median13.5
Q322
95-th percentile45.0375
Maximum177
Range176.8
Interquartile range (IQR)14.488636

Descriptive statistics

Standard deviation14.255607
Coefficient of variation (CV)0.83232172
Kurtosis10.02027
Mean17.12752
Median Absolute Deviation (MAD)6.6666667
Skewness2.2478459
Sum47580.252
Variance203.22234
MonotonicityNot monotonic
2023-08-10T21:15:48.056396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 34
 
1.2%
13 33
 
1.2%
9 32
 
1.2%
16 32
 
1.2%
7 32
 
1.2%
12 31
 
1.1%
14 29
 
1.0%
6 29
 
1.0%
17 29
 
1.0%
18.5 29
 
1.0%
Other values (887) 2468
88.8%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
177 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%
93.33333333 1
< 0.1%
89.625 1
< 0.1%
87 1
< 0.1%
85.66666667 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct1255
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-69.743243
Minimum-366
Maximum-1
Zeros0
Zeros (%)0.0%
Negative2778
Negative (%)100.0%
Memory size43.4 KiB
2023-08-10T21:15:48.126489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-202
Q1-87.666667
median-50.25
Q3-28.368881
95-th percentile-11.599062
Maximum-1
Range365
Interquartile range (IQR)59.297786

Descriptive statistics

Standard deviation62.905638
Coefficient of variation (CV)-0.90196032
Kurtosis4.8460142
Mean-69.743243
Median Absolute Deviation (MAD)25.916667
Skewness-2.0584051
Sum-193746.73
Variance3957.1193
MonotonicityNot monotonic
2023-08-10T21:15:48.193744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-70 21
 
0.8%
-14 18
 
0.6%
-35 18
 
0.6%
-49 16
 
0.6%
-46 16
 
0.6%
-42 16
 
0.6%
-55 15
 
0.5%
-26 15
 
0.5%
-29 15
 
0.5%
-31 15
 
0.5%
Other values (1245) 2613
94.1%
ValueCountFrequency (%)
-366 1
 
< 0.1%
-365 1
 
< 0.1%
-363 1
 
< 0.1%
-357 2
0.1%
-356 1
 
< 0.1%
-355 2
0.1%
-352 1
 
< 0.1%
-351 1
 
< 0.1%
-350 3
0.1%
-349 2
0.1%
ValueCountFrequency (%)
-1 8
0.3%
-2 3
 
0.1%
-2.5 1
 
< 0.1%
-2.601398601 1
 
< 0.1%
-3 6
0.2%
-3.321428571 1
 
< 0.1%
-3.330357143 1
 
< 0.1%
-3.5 2
 
0.1%
-4 5
0.2%
-4.144444444 1
 
< 0.1%

qty_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct204
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.939885
Minimum0
Maximum9014
Zeros1481
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size43.4 KiB
2023-08-10T21:15:48.264733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile96.3
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation290.45597
Coefficient of variation (CV)8.3130201
Kurtosis572.76241
Mean34.939885
Median Absolute Deviation (MAD)0
Skewness21.645043
Sum97063
Variance84364.668
MonotonicityNot monotonic
2023-08-10T21:15:48.327891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
53.3%
1 131
 
4.7%
2 118
 
4.2%
3 82
 
3.0%
4 72
 
2.6%
6 63
 
2.3%
5 56
 
2.0%
12 45
 
1.6%
8 39
 
1.4%
9 38
 
1.4%
Other values (194) 653
23.5%
ValueCountFrequency (%)
0 1481
53.3%
1 131
 
4.7%
2 118
 
4.2%
3 82
 
3.0%
4 72
 
2.6%
5 56
 
2.0%
6 63
 
2.3%
7 38
 
1.4%
8 39
 
1.4%
9 38
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

Interactions

2023-08-10T21:15:45.860894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.456969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.166161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.877749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.805616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.548119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.299264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.053295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.785664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.506540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.190913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.942075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.917443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.517755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.223095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.132880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.864890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.607687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.361015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.112804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.843339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.561558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.250372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.002649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.972575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.574456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.277718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.195112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.927208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.668102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.419473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.171468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.903251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.615124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.310326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.062422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.041499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.633600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.338232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.254755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.989216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.734598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.486276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.233514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.963825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.674839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.372646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.126381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.103981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.695808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.401508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.317983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.052825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.799490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.550914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.297881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.026810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.735540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.436373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.373297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.165421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.756668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.464141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.380921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.116774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.863635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.618325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.362785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.089258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.796256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.504467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.437466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.228110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.819246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.525968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.445734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.181167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.930364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.682022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.426630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.153849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.856050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.568765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.501064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.287609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.878269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.586776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.506797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.243271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.992682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.745912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.489910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.214694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.912961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.632077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.564007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.345096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.937513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.646703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.566679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.306547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.055061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.807674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.549772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.274038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.969741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.693038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.623774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.399879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:37.990717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.699923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.621724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.363837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.112050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.869237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.605479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.328182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.020491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.751970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.680553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.462608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.052246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.762281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.685028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.427621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.175092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.931784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.667216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.389537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.080649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.819534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.742450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:46.522003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.112641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:38.822526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:39.747505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:40.490221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.241162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:41.996239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:42.729829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:43.452325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.139085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:44.884338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T21:15:45.805290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-10T21:15:48.384837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idgross_revenuerecency_daystotal_orderstotal_products_orderedtotal_quantityfrequencyavg_basket_size_quantityavg_ticketavg_basket_size_productsavg_recency_daysqty_returns
customer_id1.000-0.0860.0130.0140.012-0.0790.013-0.121-0.141-0.002-0.012-0.057
gross_revenue-0.0861.000-0.3750.7640.7240.9210.2540.6010.2710.1050.3830.458
recency_days0.013-0.3751.000-0.450-0.394-0.367-0.123-0.1050.0360.003-0.215-0.185
total_orders0.0140.764-0.4501.0000.6600.7040.3170.1270.089-0.1780.4500.425
total_products_ordered0.0120.724-0.3940.6601.0000.7100.1960.404-0.3810.5470.2870.327
total_quantity-0.0790.921-0.3670.7040.7101.0000.2350.7600.1930.1490.3540.423
frequency0.0130.254-0.1230.3170.1960.2351.0000.0220.083-0.1670.9090.178
avg_basket_size_quantity-0.1210.601-0.1050.1270.4040.7600.0221.0000.1960.3850.0810.211
avg_ticket-0.1410.2710.0360.089-0.3810.1930.0830.1961.000-0.6390.1280.188
avg_basket_size_products-0.0020.1050.003-0.1780.5470.149-0.1670.385-0.6391.000-0.175-0.066
avg_recency_days-0.0120.383-0.2150.4500.2870.3540.9090.0810.128-0.1751.0000.384
qty_returns-0.0570.458-0.1850.4250.3270.4230.1780.2110.188-0.0660.3841.000

Missing values

2023-08-10T21:15:46.603388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-10T21:15:46.704086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daystotal_orderstotal_products_orderedtotal_quantityfrequencyavg_basket_size_quantityavg_ticketavg_basket_size_productsavg_recency_daysqty_returns
0178505391.21372.034.0297.01733.017.00000050.97058818.1522220.617647-35.50000040.0
1130473232.5956.09.0171.01390.00.028302154.44444418.90403511.666667-27.25000035.0
2125836705.382.015.0232.05028.00.040323335.20000028.9025007.600000-23.18750050.0
313748948.2595.05.028.0439.00.01792187.80000033.8660714.800000-92.6666670.0
415100876.00333.03.03.080.00.07317126.666667292.0000000.333333-8.60000022.0
5152914623.3025.014.0102.02102.00.040115150.14285745.3264714.357143-23.20000029.0
6146885630.877.021.0327.03621.00.057221172.42857117.2197867.047619-18.300000399.0
7178095411.9116.012.061.02057.00.033520171.41666788.7198363.833333-35.70000041.0
81531160767.900.091.02379.038194.00.243316419.71428625.5434646.230769-4.144444474.0
9160982005.6387.07.067.0613.00.02439087.57142929.9347764.857143-47.6666670.0
customer_idgross_revenuerecency_daystotal_orderstotal_products_orderedtotal_quantityfrequencyavg_basket_size_quantityavg_ticketavg_basket_size_productsavg_recency_daysqty_returns
425517290525.243.02.0102.0404.00.142857202.0000005.14941246.000000-13.00.0
42611478577.4010.02.03.084.00.33333342.00000025.8000001.000000-5.00.0
426217254272.444.02.0112.0252.00.166667126.0000002.43250050.000000-11.00.0
427617232421.522.02.036.0203.00.153846101.50000011.70888915.000000-12.00.0
427717468137.0010.02.05.0116.00.40000058.00000027.4000002.500000-4.00.0
428013596697.045.02.0166.0406.00.250000203.0000004.19903666.500000-7.00.0
4285148931237.859.02.073.0799.00.666667399.50000016.95684936.000000-2.00.0
430414126706.137.03.015.0508.00.750000169.33333347.0753334.666667-3.050.0
4308135211092.391.03.0435.0733.00.300000244.3333332.511241104.000000-4.50.0
431315060301.848.04.0120.0262.02.00000065.5000002.51533320.000000-1.00.0